Evidence that staphylococcal superantigens promote within-patient bacterial persistence following post-operative surgical site infection
Bibliographic record
Abstract
ABSTRACT Staphylococcus aureus is a predominant cause of post-operative surgical site infections and persistent bacteremia. Here, we describe a patient who experienced three episodes of S. aureus infection over a period of 4 months following a total knee arthroplasty. The initial bloodstream isolate (SAB-0429) was a clonal complex 5 (CC5) and methicillin-resistant S. aureus (MRSA), whereas two subsequent isolates (SAB-0485 and SAB-0495) were CC5 isolates but methicillin-sensitive S. aureus . The two latter isolates harbored a plasmid encoding three superantigen genes that were not present in the primary MRSA isolate. SAB-0485 and SAB-0495 both expressed the plasmid-encoded staphylococcal enterotoxin R exotoxin and demonstrated increased superantigen activity compared with SAB-0429. Compared to SAB-0429, the latter isolates also demonstrated an increased bacterial burden in a mouse bacteremia model that was dependent on increased interferon-γ production. Curing of the plasmid from SAB-0485 reduced this virulence phenotype. These findings suggest that the superantigen exotoxins may provide a selective advantage in chronic post-surgical infections. IMPORTANCE In this study, we investigated bacterial isolates from a patient who experienced three recurrent S. aureus infections over a 4 month period following total knee arthroplasty. Genomic and phenotypic characterization of these isolates revealed that they all belonged to clonal complex 5, yet the latter two strains contained an additional plasmid encoding superantigen exotoxins. Subsequent experimental infection experiments in mice demonstrated that the plasmid-encoded superantigens exacerbated bacteremia by promoting liver abscess formation. These experiments suggest that despite appropriate antibiotic therapy, bacterial superantigens may be able to promote persistent infection following post-surgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".